An Ag-graphene layers-coated H-shaped photonic crystal fiber (PCF) surface plasmon resonance (SPR) sensor with a U-shaped grooves open structure for refractive index (RI) sensing is proposed and numerically simulated by the finite element method (FEM). The designed sensor could solve the problems of air-holes material coating and analyte filling in PCF. Two big air-holes in the x-axis produce a birefringence phenomenon leading to the confinement loss and sensitivity of x-polarized light being much stronger than y-polarized. Graphene is deposited on the layer of silver in the grooves; its high surface to volume ratio and rich π conjugation make it a suitable dielectric layer for sensing. The effect of structure parameters such as air-holes size, U-shaped grooves depth, thickness of the silver layer and number of graphene layers on the sensing performance of the proposed sensor are numerical simulated. A large analyte RI range from 1.33 to 1.41 is calculated and the highest wavelength sensitivity is 12,600 nm/RIU. In the linear RI sensing region of 1.33 to 1.36; the average wavelength sensitivity we obtained can reach 2770 nm/RIU with a resolution of 3.61 × 10⁻⁵ RIU. This work provides a reference for developing a high-sensitivity; multi-parameter measurement sensor potentially useful for water pollution monitoring and biosensing in the future.

An extended robot–world and hand–eye calibration method is proposed in this paper to evaluate the transformation relationship between the camera and robot device. This approach could be performed for mobile or medical robotics applications, where precise, expensive, or unsterile calibration objects, or enough movement space, cannot be made available at the work site. Firstly, a mathematical model is established to formulate the robot-gripper-to-camera rigid transformation and robot-base-to-world rigid transformation using the Kronecker product. Subsequently, a sparse bundle adjustment is introduced for the optimization of robot–world and hand–eye calibration, as well as reconstruction results. Finally, a validation experiment including two kinds of real data sets is designed to demonstrate the effectiveness and accuracy of the proposed approach. The translation relative error of rigid transformation is less than 8/10,000 by a Denso robot in a movement range of 1.3 m × 1.3 m × 1.2 m. The distance measurement mean error after three-dimensional reconstruction is 0.13 mm.

To address the challenge of low diagnostic accuracy in rolling bearing fault diagnosis under varying operating conditions, this paper proposes a novel method integrating the synchronized wavelet transform (SWT) with an enhanced Vision Transformer architecture, referred to as ResCAA-ViT. The SWT is first applied to process raw vibration signals, generating high-resolution time–frequency maps as input for the network model. By compressing and reordering wavelet transform coefficients in the frequency domain, the SWT enhances time–frequency resolution, enabling the clear capture of instantaneous changes and local features in the signals. Transfer learning further leverages pre-trained ResNet50 parameters to initialize the convolutional and residual layers of the ResCAA-ViT model, facilitating efficient feature extraction. The extracted features are processed by a dual-branch architecture: the left branch employs a residual network module with a CAA attention mechanism, improving sensitivity to critical fault characteristics through strip convolution and adaptive channel weighting. The right branch utilizes a Vision Transformer to capture global features via the self-attention mechanism. The outputs of both branches are fused through addition, and the diagnostic results are obtained using a Softmax classifier. This hybrid architecture combines the strengths of convolutional neural networks and Transformers while leveraging the CAA attention mechanism to enhance feature representation, resulting in robust fault diagnosis. To further enhance generalization, the model combines cross-entropy and mean squared error loss functions. The experimental results show that the proposed method achieves average accuracy rates of 99.96% and 96.51% under constant and varying load conditions, respectively, on the Case Western Reserve University bearing fault dataset, outperforming other methods. Additionally, it achieves an average diagnostic accuracy of 99.25% on a real-world dataset of generator non-drive end bearings in wind turbines, surpassing competing approaches. These findings highlight the effectiveness of the SWT and ResCAA-ViT-based approach in addressing complex variations in operating conditions, demonstrating its significant practical applicability.

A temperature fiber sensor based on tapered fiber Bragg grating (tapered FBG) fabricated by femtosecond laser has been proposed and realized with good reproducibility. Firstly, the fiber taper with 25 μm diameter and 1000 μm length is fabricated by arc-discharge elongation using two standard single-mode fibers. Secondly, two first-order FBGs are fabricated in tapered and non-tapered fiber regions for comparison. Both FBGs are point-by-point direct-written by femtosecond laser, and the grating lengths are 1000 μm. Thirdly, a temperature experiment is performed using a heating chamber, and experimental results show that in the range of 30~350 °C, the temperature sensitivity of the tapered FBG has increased from 11.0 pm/°C to 12.3 pm/°C. The tapered FBG proposed here can be further configured for sensing other parameters in physical, chemical, and biomedical applications.

A spatially variable retardation device, an SQWP, is designed to generate polarization vortex beams. The transformation of Laguerre–Gaussian beams by the SQWP is further studied, and it is found that the SQWPs can also be used to generate helical beams and measure the topological charges of helical beams.